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Evaluating the Impacts of State Energy Efficiency: Status of State- and Sub-State-Level Energy Policy Impact Analysis

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Evaluating the Impacts of State Energy Efficiency: Status of State- and Sub-State-Level Energy Policy Impact Analysis

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  1. Modeling the Impacts of State Energy PoliciesBarry Rubin, Zachary Wendling, David Warren, SanyaCarley, and Kenneth RichardsSchool of Public and Environmental AffairsIndiana UniversityBloomington, IN 47405Presented at the 32nd USAEE/IAEE North American Conference, Anchorage, Alaska, July, 2013

  2. Evaluating the Impacts of State Energy Efficiency: Status of State- and Sub-State-Level Energy Policy Impact Analysis • US energy policy is primarily driven by state governments. • Decisions at the state and sub-state levels are based on preconceptions rather than detailed policy analysis. • There is a compelling need for tools that can help state and local governments and business leaders evaluate alternative energy pathways.

  3. Evaluating the Impacts of State Energy Efficiency: An Econometric Modeling Approach • Over the past fouryears, our research team at Indiana University has created a framework for evaluating the impact of alternative energy policy scenarios. • We have constructed an econometric, simultaneous, multi-equation econometric model that addresses the connection between energy consumption, energy prices, and economic activityfor Indiana. • In this presentation, we describe the estimation and simulation results for the sample period, and • provide the results of applying the state model to analyze a Demand Side Management (DSM) policy.

  4. Evaluating the Impacts of State Energy Efficiency: An Econometric Modeling Approach • The model will eventually include 10 homogeneous multi-county regions. However, the research reported here focuses on our state-level model and two regions (South Bend and Indianapolis), each with 30 stochastic equations. • The endogenous variables of the model include employment and earnings for ten economic sectors, and energy consumption disaggregated across three fuel types and four end use categories. • The model also addresses unemployment, nonwage income, total personal income, and GDP per capita.

  5. Evaluating the Impacts of State Energy Efficiency: An Econometric Modeling Approach • The Indiana econometric model was estimated with annual data over the period 1977-2011. • It is structured in a dynamic, simultaneous framework that allows tracing the effect of changes in exogenous and policy variables over the course of successive periods. • The explicit linkage of diverse economic sectors via this structure simulates the interdependent nature of the economy, allowing the effects of energy and environmental policies to be transmitted throughout the model.

  6. Evaluating the Impacts of State Energy Efficiency: An Econometric Modeling Approach • Data sources include the U.S. Bureau of Labor Statistics, the Bureau of Economic Analysis, the Energy Information Administration (EIA), the Annual Survey of Manufactures and the Indiana Business Research Center. • Exogenous variables include population, climatic variables, national level economic data, and, most importantly, energy prices for natural gas, motor gas, and electricity. • Figure 1 graphically depicts the simultaneous nature of the model by identifying the various linkages across sectors and equations.

  7. Evaluating the Impacts of State Energy Efficiency

  8. Evaluating the Impacts of State Energy Efficiency: Stochastic Equations, Estimation and Simulation Results • OLS estimation was used due to the tradeoff between simultaneous equation bias and the potential propagation of data errors with simultaneous equation estimation techniques in regional models. • The following tables identify the endogenous and exogenous variables of the model, including the specific variables in each equation. • Mean Absolute Percent Errors (MAPEs) are provided for each equation, as are graphs of actual and predicted values for selected endogenous variables from the sample period simulations.

  9. Evaluating the Impacts of State Energy Efficiency: Stochastic Equations, Estimation and Simulation Results • MAPE values indicate the average error for each endogenous variable when the model is “turned on” to simulate the sample period values with which the equations were fit. • They provide an indication of the model’s performance and predictive ability, in contrast to single equation results which do not include the simultaneous interaction of the endogenous variables. • MAPE values under 5% are generally considered excellent, between 5-10% very good, 10-15% good to mediocre, 15-20% potentially problematic, and >20% poor.

  10. Evaluating the Impacts of State Energy Efficiency: Endogenous and Exogenous Variables

  11. Evaluating the Impacts of State Energy Efficiency: Endogenous and Exogenous Variables

  12. Evaluating the Impacts of State Energy Efficiency: Endogenous and Exogenous Variables

  13. Evaluating the Impacts of State Energy Efficiency: State Estimation and Simulation Results

  14. Evaluating the Impacts of State Energy Efficiency: South Bend Estimation and Simulation Results

  15. Evaluating the Impacts of State Energy Efficiency: Indianapolis Estimation and Simulation Results

  16. Evaluating the Impacts of State Energy Efficiency: Simulation Results Manufacturing employment (State)

  17. Evaluating the Impacts of State Energy Efficiency: Simulation Results Manufacturing employment (South Bend)

  18. Evaluating the Impacts of State Energy Efficiency: Simulation Results Manufacturing employment (Indianapolis)

  19. Evaluating the Impacts of State Energy Efficiency: Simulation Results Employment in services (State)

  20. Evaluating the Impacts of State Energy Efficiency: Simulation Results Employment in services (South Bend)

  21. Evaluating the Impacts of State Energy Efficiency: Simulation Results Employment in services (Indianapolis)

  22. Evaluating the Impacts of State Energy Efficiency: Simulation Results Residential electricity consumption (State)

  23. Evaluating the Impacts of State Energy Efficiency: Simulation Results Residential electricity consumption (South Bend)

  24. Evaluating the Impacts of State Energy Efficiency: Simulation Results Residential electricity consumption (Indianapolis)

  25. Evaluating the Impacts of State Energy Efficiency: Policy Analysis Policy analysis results with multi-equation models are derived via differencing a baseline forecast without the policy implemented, with a forecast in which the policy is present. The policy is translated into changes in the exogenous variables of the model. The policy that we address is the Indiana Utilities Regulatory Commission’s (IURC) Demand Side Management (DSM) ruling, passed in 2008, which requires all state jurisdictional electric utilities to achieve a two percent decrease in electricity use by 2019.

  26. Evaluating the Impacts of State Energy Efficiency: Policy Analysis This policy is the only requirement for DSM in Indiana and has not been evaluated in a systematic way with respect to its impacts on the Indiana economy. It also serves as an example of how the econometric model can be utilized for policy analysis. We simulated the implementation of DSM by treating the electricity consumption variables as exogenous, setting the 2019 values two percent below the 2008 levels, and using a linear rate of decline between 2008 and 2019. The following charts compare selected elements of the baseline and policy forecasts, illustrating the percent impact of the policy.

  27. Evaluating the Impacts of State Energy Efficiency: Economic Impacts State impacts, percent difference versus the baseline forecast, 2019:

  28. Evaluating the Impacts of State Energy Efficiency: Economic Impacts Vary by Location Percent difference in total employment versus the baseline, 2019:

  29. Evaluating the Impacts of State Energy Efficiency: Economic Impacts Vary by Location Percent difference in total earnings per employee versus the baseline, 2019:

  30. Evaluating the Impacts of State Energy Efficiency: Economic Impacts Vary by Location Percent difference in employment in services versus the baseline, 2019:

  31. Evaluating the Impacts of State Energy Efficiency: Natural Gas Consumption Forecasts Percent change in industrial natural gas consumption versus the baseline forecast, 2019

  32. Evaluating the Impacts of State Energy Efficiency: Natural Gas Consumption Forecasts Percent change in commercial natural gas consumption versus the baseline, 2019

  33. Evaluating the Impacts of State Energy Efficiency: Natural Gas Consumption Forecasts Percent change in residential natural gas consumption versus the baseline, 2019

  34. Evaluating the Impacts of State Energy Efficiency: Economic Impact Results Summary Implementation of the IURC DSM order is projected to have a significant impact on the state economy by 2019, with varying magnitudes by region: Total state employment is expected to rise by 107,000 (3.2%) as compared to the baseline forecast. South Bend and Indianapolis are projected to gain 25,000 (9.1%) and 55,000 (5.5%) jobs, respectively. Earnings per employee are projected to rise by about $1,200 (2.7%). Gains of $2,500 (4.9%) and $3,900 (6.7%) are forecast for South Bend and Indianapolis. GDP per capita goes up $3,300 (8.5%), with an increase of $6,700 (20.7%) and $2,400 (5.2%) forecast for South Bend and Indianapolis.

  35. Evaluating the Impacts of State Energy Efficiency: Energy Impact Results Summary As for energy consumption, as compared to the baseline forecast: The state is forecast to increase industrial, commercial, and residential natural gas consumption by 11.5%, 0.1%, and 1.8%, respectively. Again, impacts in South Bend and Indianapolis vary in magnitude, with modest increases in the three natural gas end use sectors for South Bend (3.5%, 6.9%, and 4.1%). Indianapolis is projected to see small increases in commercial and residential natural gas consumption (0.9% and 1.7%) but a large increase in industrial natural gas consumption (23.5%).

  36. Evaluating the Impacts of State Energy Efficiency: Conclusions This research represents an advance in the ability to identify potential impacts of alternative energy policies by: producing useable policy analysis results and thus serving as a “proof of concept” for the econometric modeling approach, disaggregating the potential impacts of critical policy alternatives by economic sector, fuel type, and end use sector, having the potential to inform public and private sector decision-makers as to impacts on the state economy and specific industries, and assisting energy utilities in planning for the future. No other policy analysis framework provides such an extensive range of impact analysis results, nor are other policy analysis tools as customizable to specific state or local economies.

  37. Evaluating the Impacts of State Energy Efficiency: Extensions Planned extensions/further research: addition of eight more sub-state, multi-county regions interacting with state-level model, linking the econometric model to MARKAL for addressing energy technology and more accurately portraying alternative policies as translated into exogenous policy variables, integrating GIS to incorporate land use and transportation impacts and feedback, and eventually extending to other state and subregions.

  38. Evaluating the Impacts of State Energy Efficiency: Limitations Limitations: the model is based on historical data and relationships, implying that forecasts are sensitive to structural shifts, this extensive a modeling effort requires significant resources to construct and test the model, and the accuracy of forecasts and policy analysis depends on the quality of state-level data.

  39. Evaluating the Impacts of State Energy Efficiency: Interesting Development We recently completed, in tandem with the Indiana Business Research Center, an analysis of a proposed coal gasification plant for a major Indiana utility using a previous version of our state model.

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